Removing high/low frequency noise #46
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I have added the code which does the following: 1. Transform the data to the frequency domain using FFT. 2. Apply a bandpass filter by zeroing out components outside the specified frequency range. 3. Perform the inverse FFT to return to the time domain, giving a cleaned-up version of the data with reduced noise.
Removed the section for plotting the difference
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@ubsuny/cp1-24 Anyone wants to review this? |
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@Cosmos491 You have to resolve the merge conflicts before this can be merged as we discussed in class. |
This unit test module has the following functions: 1. setUp: Generates synthetic data with a known mix of low and high-frequency components. 2. test_remove_low_frequency: Tests removing frequencies below 0.02, verifying that low frequencies are removed by checking the FFT result. 3. test_remove_high_frequency: Tests removing frequencies above 0.02, ensuring that high frequencies are removed. 4. test_full_bandpass_filter: Tests both low and high frequency filtering by setting a bandpass filter between 0.01 and 0.05.
Added a data variable for the example
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dhamalakamal
reviewed
Nov 8, 2024
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@dhamalakamal I will work on them now. Thanks |
added the remove noise function in the same preparation.py module
added the unit tests function to remove noise
dhamalakamal
approved these changes
Nov 11, 2024
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The code does the following steps:
This example retains frequencies between 0.001 Hz and 0.05 Hz, effectively filtering out lower and higher frequencies. I have to adjust low_freq and high_freq based on the signal characteristics: I need to experiment with different thresholds to find suitable values for the collected data.